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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

個人信用行為評分模式之研究--以現金卡用戶為例

詹育晟 Unknown Date (has links)
授信業務為金融業者主要業務之一,也是金融業獲利的重要來源。為了減少逾期放款比率過高,嚴重影響金融業的獲利。因此,金融業者建立了一套標準的審核機制,也就是信用風險的評估模式,此模式應用在未核貸給客戶之前,可先行評估客戶的信用風險大小,以作為貸款批准、貸款額度多寡的依據。評估模式的建立大多採用信用評分的方式,而信用評分可分為應用(申請)評分(Application Scoring)及行為評分(Behavior Scoring)兩部分。一般信用風險評估模式大部分採用的是應用(申請)評分,屬於事前評估預測。而本研究採用事後的行為評分,針對如何減少現金卡用戶在核准後,由於某些特定因素造成違約逾期繳款,以致於金融業者要進行催款甚至上法院進行官司訴訟,造成金融業者額外負擔的情況,故本研究針對核准之後的現金卡用戶行為,期能建立一套有效的評分模式,以作為金融業者採取降低損失措施的參考依據。 本研究以國內某金融機構的現金卡用戶為研究對象,採用進件系統中的客戶基本資料以及聯合徵信中心的信用資料,做為評估分析的依據,利用邏輯斯迴歸(Logistic Regression, LR)統計方法來進行模式之建置。 研究實證結果歸納如下: 一、顯著變數為性別、教育程度、最近三個月內照會次數、行外金融機構總借款借款餘額/行外金融機構總借款訂約金額、信用卡有效家數、現金卡張數、現金卡總訂約金額、現金卡總借款餘額/現金卡總訂約金額、本行目前額度/本行訂約額度等9個。 二、聯合徵信中心資料具有相當程度之影響力 三、動用比率實為具有相當程度影響力之風險變數
2

複雜抽樣設計下邏輯斯迴歸模式之分析

劉國輝 Unknown Date (has links)
當反應變數是二元(binary)時,邏輯斯迴歸(logistic regression)可幫助我們建立解釋變數與反應變數間的關係。然而,一般在進行邏輯斯迴歸分析時,總是假設樣本資料是由簡單隨機抽樣(simple random sampling)所取得,亦即所有的樣本皆具有相同的抽樣權重(sampling weights)。不過,在實務上,許多的大型抽樣調查都是採用複雜抽樣方法(complex sampling method)來抽取樣本。例如:採用多階段抽樣(multistage sampling)結合分層抽樣(stratified sampling)或是群集抽樣(cluster sampling)的方式來進行抽樣。由於樣本不再是以簡單隨機抽樣所取得,因此,統計分析的方式可分為兩類:一類乃設計導向(design-based);另一類則為模式導向(model-based)。其中,若將抽樣調查的抽樣設計方式以及樣本的代表性與統計模式的估計或檢定等推論過程相結合,則其屬於設計導向之方式。反之,若忽略這些因素,則相當於視調查資料來自於簡單隨機樣本,仍遵循一般的程序進行分析,則稱之為模式導向。本論文旨在探討如何以設計導向的方法,進行複雜抽樣方法所取得樣本資料的邏輯斯迴歸分析。
3

選舉模式預測之探討與分析--民國八十六年縣市選舉

劉遠浩 Unknown Date (has links)
隨著台灣民主政治的成長、發展與進步,政治訊息與政治行為已經成為民眾在日常生活中一部分。而在這些政治現象的研究中,「選舉研究」,尤其是投票行為的研究及選與預測的論述,也隨著問卷設計、抽樣技術與分析方法的進步而有著蓬勃的發展。 本文即是利用民國八十六年縣市長選舉的民意調查資料,將兩種得票率預測模型:「政治版圖預測模型」與「邏輯斯(logistic)預測模型」做一整合研究與結果的比較,並且利用其它預測模型的優點與長處,配合已有的經驗法則和選舉的結果,不僅比對其預測結果的差異大小,也嘗試著找出另一更準確,更有效的預測模型。 在比較了預測方法的準確性之後,發現「政治版圖模型」在只有兩黨競爭的縣市有著相當佳的預測結果;而「邏輯斯(logistic)預測模型」卻是明確得表現了民意調查中選民的意向並在變天的縣市中有著較好的表現。但是如果在候選人實力差不多的選區中,則是建議採用以這兩種模型為主體的混合型模型來進行預測,其預測結果確較前兩種模型的預測來得好。
4

公司治理變數是否會影響銀行評等結果? / Does corporate governance variables influence the outcome of bank rating?

吳夢萱 Unknown Date (has links)
本研究以公司治理變數與銀行評等結果之關係為研究對象,並以累積邏輯斯迴歸模型(Cumulative Logistic Regression Model)研究2004年至2010年的31間台灣的上市銀行,探討公司治理對於公司決策的影響,及其是否會進而影響公司的表現,最終並改變公司的評等結果。 本文由股權結構以及董事會的組成這兩大結構來探討公司治理對銀行的評等影響,股權結構的部分主要選擇董監持股、外部法人董監持股、董監持股質押比例、大股東持股、經理人持股、最大外部股東、盈餘席次偏離比、股份席次偏離比等變數,而董事會的組成方面則選擇董事及監事兼任占經理人比例、股監事席次、獨立董監席次等變數來探討。研究發現董監事、大股東及外部法人董監的持股比率越高,對公司的評等結果有正向影響;反之,經理人之持股比率則與公司的評等結果呈負向關係。此外,獨立董監占董事會的席次越高,與公司的評等結果存在正向的關係。 / The bank rating system used to only consider the financial variables instead of corporate governance variables, but whether the corporate governance variables can influence the strategies of managers and the performance of the firm, then further change the outcome of the bank rating had never been considered. This thesis is trying to use cumulative logistic model figure out whether there exist any connection between corporate governance variables and bank rating outcome. This thesis considered 9 difference corporate variables that can be divided into two different sectors, which are ownership structure and boardroom structure. The conclusion of this thesis is that the majority shareholder's holding、Supervisors and Directors' holding and institutional Supervisor and Director's holding、Managers' holding and the number of independent director and supervisor will influence the outcome of the bank rating.
5

選舉預測模型之研究-以公元2000年總統大選為例 / The Study of The Election Prediction Model─Take The 2000 Presidential Election for Example

蘇淑枝, Su, Shu-Chih Unknown Date (has links)
中華民國第十任總統選舉結果於民國八十九年三月十八日揭曉,這場眾所矚目的選舉終告落幕,然而對選舉研究工作者而言卻是新的開始。選舉預測居選戰中重要的一環,也是研究選舉的學者關心的問題,更提供了一個驗證選民投票行為理論的絕佳機會,近來國內相關論述已有相當成果。但由於它在投票結束,便有答案,其挑戰程度不言而喻。因此,如何結合理論、方法及事實三者為一體的努力,對選舉預測更是別具意義。 本篇研究之範圍,是以公元2000年總統大選為例,對選舉預測工作做更深層的探討,且檢驗邏輯斯預測模型(Logistic Regression Model)及模糊統計(Fuzzy Statistics)分析在本次總統選舉的預測力,考量本次總統選舉中各項可能影響選情的因素,進一步建構選舉預測模式,然而兩種預測模式的初步預測結果並不佳,經過棄保效應的可能性調整後,預測誤差已大幅降低,其中模糊統計(Fuzzy Statistics)分析預測結果經棄保效應調整後,與實際開票結果相當接近,因此與邏輯斯預測模型相較,模糊統計分析的應用對未表態選民投票意向的預測力較佳。一套完整的選舉預測模型研究,應包含問卷設計、抽樣訪問、資料處理、加權除錯、模型設計與預測評估等整套研究流程,然而在本次總統大選中,由於三強激戰,影響選情因素相當複雜,最後此兩種選舉預測模式皆無法獲致精確的預測結果。因此,我們期待選舉預測模型的建構,能突破主客觀環境的侷限,進一步達到「準」與「穩」的要求。 / With the successful staging of the 2000 presidential elections in Taiwan, scholars have been presented with a new opportunity to test their theories. Electoral predictions are an important field within the study of elections and have been among the most keenly studied questions over the past few years. Unlike many other research topics, there is an absolute standard for election predictions: the election results. Thus, combining theory, methodology, and facts to obtain a meaningful result is no simple task. This thesis attempts to predict the 2000 presidential election using both a logistic regression model and a fuzzy statistics model. After constructing models which includes all kinds of different variables that might influence the electoral outcome, we find that neither the logistic regression model nor the fuzzy statistics model is particularly accurate. However, after accounting for the effects of strategic voting, model error decreases dramatically. In particular, after including provisions for strategic voting, the fuzzy statistics model is improved to the point that its predictions are extremely close to the actual outcome. Thus, we show that the fuzzy statistics model is superior to the logistic regression model in analyzing the vote choices of undecided voters. Research on electoral predictions should include such aspects as questionnaire design, sampling, interviewing, data processing, weighting, data cleaning, model design, and evaluation of the prediction. However, because this election featured a particularly intense three way race, the factors affecting the electoral outcome were both numerous and intertwined in complex ways. Unfortunately, it is impossible to evaluate our electoral predictions of the two models precisely. We hope that in the future, election prediction models will be able to break through these environmental limitations and achieve more accurate and stable predictions.
6

多期邏輯斯迴歸模型應用在企業財務危機預測之研究 / Forecasting corporate financial distress:using multi-period logistic regression model

卜志豪, Pu, Chih-Hao Unknown Date (has links)
本研究延續Shumway (2001) 從存活分析(Survival Analysis)觀點切入,利用離散型風險模型(Discrete-time Hazard Model)──亦即Shumway 所稱之多期邏輯斯迴歸模型(Multi-period Logistic Regression Model),建立企業財務危機預警模型。研究選取1986 年至2008 年間718 家上市公司,其中110 家發生財務危機事件,共計6,782 公司/年資料 (firm-year)。有別於Shumway 提出的Log 基期風險型式,本文根據事件發生率圖提出Quadratic 基期風險型式,接著利用4組(或基於會計測量,或基於市場測量)時間相依共變量 (Time-dependent Covariate)建立2 組離散型風險模型(Log 與Quadratic),並與傳統僅考量單期資料的邏輯斯迴歸模型比較。實證結果顯示,離散型風險模型的解釋變數與破產機率皆符合預期關係,而傳統邏輯斯迴歸模型則有時會出現不符合預期關係的情況;研究亦顯示離散型風險模型預測能力絕大多數情況下優於傳統邏輯斯迴歸模型,在所有模型組合中,以Quadratic 基期風險型式搭配財務變數、市場變數的解釋變數組合而成的離散型風險模型,擁有最佳預測能力。 / Based on the viewpoint of survival analysis from Shumway (2001), the presentthesis utilizes discrete-time hazard model, also called multi-period logistic regression model, to forecast corporate financial distress. From 1986 to 2008, this research chooses 718 listed companies within, which includes 110 failures, as the subjects, summing to 6,782 firm-year data. Being different from Shumway’s log baseline hazard form,we proposed to use quadratic baseline hazard form according to empirical evidence. Then, four groups of time-dependent covariates, which are accounting-based measure or market-based measure, are applied to build two sets of discrete-time hazard model, which is compared with the single-period logistic regression model. The results show that there exists the expected relationship between covariates and predict probability in discrete-time hazard model, while there sometimes lacks it in single-period logistic regression model. The results also show that discrete-time hazard model has better predictive capability than single-period logistic regression model. The model, which combines quadratic baseline hazard form with market and accounting variables, has the best predictive capability among all models.
7

資料採礦於乘用汽車產業之顧客關係管理研究 / A Study of Data Mining on Automobile Industry’s Customer Relationship Management

陳竑廷 Unknown Date (has links)
國父 孫中山先生曾說:『民生的需要,從前經濟學家都說是衣、食、住三種。照我的研究,應該有四種:於衣、食、住之外,還有一種就是行。』,在各種交通工具中,最普及的就是汽車。汽車由貴族地位的象徵,發展至福特汽車公司一家獨大,最後演變為各大汽車品牌的競爭。更因消費者意識的改變,購買汽車時考慮的不再僅是量產速度、購買價格。在現今生產技術成熟,沒有一家汽車公司具壓倒性優勢的情況下,品牌的因素將會是消費者進行購買決策時一個重要的指標。 本研究欲透過國內六大汽車品牌之顧客關係資料,利用資料採礦模型,瞭解品牌形象、廣告印象及人口統計變數與購買意願之關係,進一步探討各汽車品牌之消費者忠誠度、客群分布與品牌差異,期能在汽車品牌公司百家爭鳴情況下,分析出消費者於不同汽車品牌之品牌知覺,提供汽車品牌之購買意願模型與後續研究參考。
8

含存活分率之貝氏迴歸模式

李涵君 Unknown Date (has links)
當母體中有部份對象因被治癒或免疫而不會失敗時,需考慮這群對象所佔的比率,即存活分率。本文主要在探討如何以貝氏方法對含存活分率之治癒率模式進行分析,並特別針對兩種含存活分率的迴歸模式,分別是Weibull迴歸模式以及對數邏輯斯迴歸模式,導出概似函數與各參數之完全條件後驗分配及其性質。由於聯合後驗分配相當複雜,各參數之邊際後驗分配之解析形式很難表達出。所以,我們採用了馬可夫鏈蒙地卡羅方法(MCMC)中的Gibbs抽樣法及Metropolis法,模擬產生參數值,以進行貝氏分析。實證部份,我們分析了黑色素皮膚癌的資料,這是由美國Eastern Cooperative Oncology Group所進行的第三階段臨床試驗研究。有關模式選取的部份,我們先分別求出各對象在每個模式之下的條件預測指標(CPO),再據以算出各模式的對數擬邊際概似函數值(LPML),以比較各模式之適合性。 / When we face the problem that part of subjects have been cured or are immune so they never fail, we need to consider the fraction of this group among the whole population, which is the so called survival fraction. This article discuss that how to analyze cure rate models containing survival fraction based on Bayesian method. Two cure rate models containing survival fraction are focused; one is based on the Weibull regression model and the other is based on the log-logistic regression model. Then, we derive likelihood functions and full conditional posterior distributions under these two models. Since joint posterior distributions are both complicated, and marginal posterior distributions don’t have closed form, we take Gibbs sampling and Metropolis sampling of Markov Monte Carlo chain method to simulate parameter values. We illustrate how to conduct Bayesian analysis by using the data from a melanoma clinical trial in the third stage conducted by Eastern Cooperative Oncology Group. To do model selection, we compute the conditional predictive ordinate (CPO) for every subject under each model, then the goodness is determined by the comparing the value of log of pseudomarginal likelihood (LPML) of each model.

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